Delays and States in Dataflow Models of Computation

Abstract : Dataflow Models of Computation (MoCs) have proven efficient means for modeling computational aspects of Cyber-Physical System (CPS). Over the years, diverse MoCs have been proposed that offer trade-offs between expressivity, conciseness, predictability, and reconfigurability. While being efficient for modeling coarse grain data and task parallelism, state-of-the-art dataflow MoCs suffer from a lack of semantics to benefit from the lower grained parallelism offered by hierarchically modeled nested loops. In this paper 1 , a meta-model called State-Aware Dataflow (SAD) is proposed that enhances a dataflow MoC, introducing new semantics to take advantage of such nested loop parallelism. SAD extends the semantics of the targeted MoC with unambiguous data persistence scope. The extended expressiveness and conciseness brought by the SAD meta-model are demonstrated with a reinforcement learning use-case.
Type de document :
Communication dans un congrès
SAMOS XVIII, Jul 2018, Pythagorion, Greece. 〈10.1145/3229631.3229645〉
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Contributeur : Florian Arrestier <>
Soumis le : lundi 20 août 2018 - 10:02:14
Dernière modification le : jeudi 7 février 2019 - 14:31:43
Document(s) archivé(s) le : mercredi 21 novembre 2018 - 12:20:59


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Florian Arrestier, Karol Desnos, Maxime Pelcat, Julien Heulot, Eduardo Juarez, et al.. Delays and States in Dataflow Models of Computation. SAMOS XVIII, Jul 2018, Pythagorion, Greece. 〈10.1145/3229631.3229645〉. 〈hal-01850252〉



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